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CAMANet : class activation map guided attention network for radiology report generation
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Wang, Jun, Bhalerao, Abhir, Yin, Terry, See, Simon and He, Yulan (2024) CAMANet : class activation map guided attention network for radiology report generation. IEEE Journal of Biomedical and Health Informatics . pp. 1-12. doi:10.1109/jbhi.2024.3354712 ISSN 2168-2194. (In Press)
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Official URL: https://doi.org/10.1109/jbhi.2024.3354712
Abstract
Radiology report generation (RRG) has gained increasing research attention because of its huge potential to mitigate medical resource shortages and aid the process of disease decision making by radiologists. Recent advancements in Radiology Report Generation (RRG) are largely driven by improving a model's capabilities in encoding single-modal feature representations, while few studies explicitly explore the cross-modal alignment between image regions and words. Radiologists typically focus first on abnormal image regions before composing the corresponding text descriptions, thus cross-modal alignment is of great importance to learn a RRG model which is aware of abnormalities in the image. Motivated by this, we propose a C lass A ctivation M ap guided A ttention Net work (CAMANet) which explicitly promotes cross-modal alignment by employing aggregated class activation maps to supervise cross-modal attention learning, and simultaneously enrich the discriminative information. Experimental results demonstrate that CAMANet outperforms previous SOTA methods on two commonly used RRG benchmarks.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
SWORD Depositor: | Library Publications Router | ||||
Journal or Publication Title: | IEEE Journal of Biomedical and Health Informatics | ||||
Publisher: | IEEE | ||||
ISSN: | 2168-2194 | ||||
Official Date: | 16 January 2024 | ||||
Dates: |
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Page Range: | pp. 1-12 | ||||
DOI: | 10.1109/jbhi.2024.3354712 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | In Press | ||||
Access rights to Published version: | Restricted or Subscription Access |
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